removed tibble dependency

        return(res)

    })

    res_base_r <- Reduce(rbind, lapply(names(res_base_r), function(group) {

        res_base_r[[group]] %>% data.frame() %>% 

            tibble::rownames_to_column('feature') %>% 

        .res <- res_base_r[[group]] %>% data.frame() 

        .res$feature <- row.names(.res)

        .res <- .res %>%     

            dplyr::mutate(group = group)

    })) %>% 

        dplyr::select(feature, group, statistic, p.value)

```

Seurat supports multiple assay types. This can be specified with `seurat_assay`.

Seurat supports multiple assay types. This can be specified with 

`seurat_assay`.

```{r}

head(wilcoxauc(object_seurat, 'cell_type', seurat_assay = 'RNA'))

## Top markers

We often find it helpful to summarize what the most distinguishing features are

in group. 

We often find it helpful to summarize what the most distinguishing features

are in each group. 

```{r}

res <- wilcoxauc(exprs, y)

## groups_use

Sometimes, you don't want to test all groups in the dataset against all other 

groups. For instance, I want to compare only observations in group 'A' to those

in group 'B'. This is achieved with the groups_use argument. 

groups. For instance, I want to compare only observations in group 'A' to

those in group 'B'. This is achieved with the groups_use argument. 

```{r}

res_AB <- wilcoxauc(exprs, y, groups_use = c('A', 'B'))